The main message conveyed by the results from Table 14 is that the output from the models built for ungraded LE indeed cannot be used as an estimate of graded LE.

the relative entropy or the measure of distributional inclusion between two concepts can be used to reliably detect which concept is the hypernym in the directionality task, or to distinguish between LE and other relations in the detection task, but it leads to a poor global estimate of the LE strength for graded LE experiments.


Although MWE-aware parsers represent only a small portion of the total parsing literature, we argue that proper MWE identification can improve parser performance (Cafferkey, Hogan, and van Genabith 2007).

complex function words that have a key role in syntax may be ambiguous (e.g., by the way).


The following sections on MWE discovery, MWE identification, parsing, and MT further explain how the core tasks of MWE processing are incorporated into the use cases and vice versa.

they develop the notion of orchestration, the effort of trying to find the best entry-point for one process to help the other-for example, the optimum moment to introduce MWE identification into the parsing pipeline.


After shedding light on the properties of MWEs that are exploited by MWE approaches and the challenges they face, we are able to understand the interactions between MWE processing and the two use cases, parsing (Section 4) and MT (Section 5).

these sections will show how shared challenges and opportunities give rise to several possible orchestration scenarios and position previous work with respect to this timing issue.


Discovery enables identification, as shown by the arrow going from discovery to identification in Figure 1.

discovery helps extending and creating lexicons that are used in identification.


An incorrect identification can mislead the parser.

complex function words that have a key role in syntax may be ambiguous (e.g., up to).


This non-compositionality can take place at different levels: morphological, distributional, syntactic, semantic, and pragmatic.

some expressions display syntactic irregularity in their internal structure-that is, they are irregular with respect to a grammar.


This section describes the different orchestration strategies involving syntactic parsing and MWE processing under four subsections as a consequence of the interactions between parsing, identification, and discovery shown in Figure 1: discovery after parsing, and identification before, during, and after parsing.

it shows how the different challenges (Section 3.1 and Section 4.1) can be handled, and summarizes the reported advantages and disadvantages of each strategy.


For an interesting illustration, Baldwin (2005) proposes different morphosyntactic and syntactic patterns to extract English verb-particle constructions with valence information from raw text.

he shows the effect of using the outputs of a POS tagger, a chunker, a chunk grammar, or a parser, either individually or combined via a classifier.


Joint approaches are of great interest for MWEs having syntactic variability.

eryigit,İlbay, and Can (2011) and Vincze, Zsibrita, and Nagy (2013) state that a joint approach using a dependency parser is very successful for the identification of light-verb constructions in Turkish and in Hungarian, respectively.


Previous sections have shown the different ways identification and parsing can interact.

they show the great interest of using manually validated MWE lexicons.


Our position in this respect is clear: We do believe that (i) advancing the field should be given priority over ensuring fair competition, also because (ii) inequality is partly unsolvable and intrinsic to life.

: Equality between competitors is desirable if it does not hinder research advancement.


(2017) also mention "Unconscious overlooking of ethical concerns," actually referring to an unacceptable level of vagueness in papers.

participants may unintentionally describe their systems in an abstract and vague way due to a previously established practice in systems' descriptions.


It simply revolves around the key aspects of sharing, not only resources but also experiences, including negative ones.

with research progress in mind, we believe sharing should be encouraged and even partially enforced.


Applying this principle to shared tasks in practice, we propose making the primary competition a "public track," where participants can use any code, data, and pre-trained models they want, as long as others can then freely obtain them.

: All resources used to participate in the shared task should be subsequently shared with the community.


Every other element of the tuple U is identical to the corresponding item in the tuple U.

notice that the set of ETs is the same in both U and U , hence, the term spanning.


In this section, we show that, for every complete UG, qeq and dominance relations are equivalent.

as stated by Theorem 5, if qeq constraints are replaced with dominance relations, the solutions of the UG remain the same.


Figure 9 demonstrates this transformation for two real-life CF-UGs.

the dependencies in U are simply encoded in the edges of G. if i is the node of G corresponding to t i , an edge from i to j in G means that l q j (l 0 , if j = 0) depends on l q i in U.


The good news is that, using the machinery provided and the results obtained in the last section, we can prove this conjecture.

although the exact distribution of dominance constraints is not given, the minimum connectivity required by hypernets guarantees this property.


An important question arising is, given that it does not allow for label-to-label constraints, is Hole Semantics powerful enough to cover all coherent UGs?

given that some constraints, such as the binding constraints, are inherently label to label, how does Hole Semantics get away without it?


Formally, a tree decomposition of a graph is defined as a pair ({X i | i ∈ I}, T = (I, F)) where tree T satisfies all of the following properties.

r Vertex cover: The nodes of the tree T cover all the vertices of G: r Edge cover: Each edge in G is included in some node of T. for all r Running intersection: The nodes of T containing a given vertex of G form a connected subtree.


Our transition system was primarily motivated by these theoretical considerations, and many other definitions are possible.

our decision that vertices can only be popped from the rightmost position in the cache simplifies our analysis.


, e k with k > 0 such that r = src(e 1 ), tar(e i ) = src(e i+1 ) for all 1 ≤ i < k, and tar(e k ) = v. We use standard terminology regarding trees.

a node v is a child of a node u if out(u) ∩ in(v) = ∅.


Such an automaton is said to accept a DAG if there exists a spanning tree of the DAG that is accepted by the automaton (viewed as a tree automaton).

whenever a DAG is accepted, every other DAG obtained by adding edges is also accepted.


More results on the (non-extended) DAG automata invented in this article were proved by Blum (2015) and Blum and Drewes (2016).

an alternative proof of the regularity of path languages was given in Blum (2015) (which is simpler and more constructive, but was conceived after the proof in Section 4.1), and the polynomial decidability of the finiteness problem was proved.


Hence, paths( By the Myhill-Nerode theorem, it therefore suffices to show that the equivalence relation ∼, given by w 1 ∼ w 2 if and only if ∆(w 1 ) = ∆(w 2 ), is a right congruence.

if ∆(w 1 ) = ∆(w 2 ) and w is any string, then So, assume that ∆(w 1 ) = ∆(w 2 ) and ] containing a path p whose node labels are w 1 w .


But just as the Cocke-Kasami-Younger algorithm for context-free grammars can be thought of as a special case of intersecting a context-free language with a regular language (Bar-Hillel, Perles, and Shamir 1961), we would more generally like to be able to intersect [[G]] with any recognizable DAG language.

given an unweighted DAG automaton M, we would like to construct an HRG G such that [[G ] To discuss briefly how this can be done, we need to give a rough introduction to HRGs (adapted to the setting and terminology of the current article).


As mentioned earlier, the DAG automata of Kamimura and Slutzki (1981) can encode computations of Turing machines.

this means that their emptiness problem is undecidable.


Formally, we have cont T (b) = {src D (edg T (b)), tar D (edg T (b))}.

furthermore, we require that the mapping edg T is a bijection between the leaves of T and the edges of D. every edge of D is introduced by a unique leaf b of T. Note that, because edg T is a bijection, for every edge e ∈ E D we have that edg −1 T (e) yields the unique leaf b of T such that edg T (b) = e. In Appendix A, Theorem 8, we show that if a graph has a tree decomposition of width k, then there exists a binary tree decomposition of the same graph also having width k. Our method of binarization is illustrated in figure 16 and explained in the following.


D T is binary because the T v are treelets and each leaf [v, b]  is attached to exactly one of the original edges e ∈ E D , namely, to edg T (b).


Hence weighted c-regular expressions and weighted m-automata are clearly equivalent in the unary case.

it is possible to convert a unary weighted c-regular expression α to an equivalent weighted m-automaton A(α), using the McNaughton-Yamada construction recalled earlier.


We have aimed to develop a formalism for DAG automata that lends itself to efficient algorithms for processing semantic graphs such as Abstract Meaning Representations.

motivated by the success of finite-state methods in natural language processing, we have tried to develop a graph analog of standard finite-state automata for strings.


The first three chapters introduce the reader to the concept of metaphor, particularly the theoretical foundations profitable for approaching the problem computationally.

useful is the explanation of the related figurative devices: similes (the comparison of one thing with another of a different kind, used to make a description more emphatic or vivid, e.g., John is as brave as a lion); analogy (a comparison between one thing and another, typically for the purpose of explanation, e.g., marriage is slavery); and conceptual blending (a cognitive theory, originally developed by Gilles Fauconnier and Mark Turner [Fauconnier and Turner 2002], which refers to a set of cognitive operations for combining-or blending-words, images, and ideas in a network of "mental spaces" to create meaning, e.g., the painting in George Clooney's attic is a cue to create a conceptual blend from the input spaces 'George Clooney' and 'Dorian Gray').


The neural language model also produces the byproduct of word representations, which form the subject of Chapters 10 and 11.

chapter 10 presents approaches to learning word representations, and chapter 11 discusses the usage of word representations outside the context of neural networks, like word similarity and word analogies.


If we look at the strings of applications to b, we have f →d ( f ←a ( f →c (b)) for the reference tree and f →d ( f →c ( f ←a (b)) for the candidate; in counting the wrong choices made, we see that we made one wrong choice of applying f ←a instead of f →c ; counting such wrong choices would assign the same distance to the candidate tree as would δ SH in this case.


The second group consists of constituency parsers that make crucial use of the notion of head EDU, introduced in the shift-reduce RST parser of Sagae (2009).

5 the representations of (complex) discourse units are built from the representations of their head EDUs.


To do so, we need to determine a distance measure based on UAS and order.

how do we measure differences in order of operations?


We investigated disagreements between Annotators A and B in bridging recognition: Almost all cases are instances where one annotator identified bridging and the other one new.

frequent were borderline cases where the whole document had one major focus and subsequent NPs with a semantic relation to that focus could be seen either as new (interpretable without the major focus) or bridging.


Therefore, our cascading collective classification system, shown in Figure 3, combines binary classifiers for minority categories and a collective classifier for all categories in a pipeline.

for the five classes mediated/function, mediated/aggregate, mediated/comparative, mediated/bridging, and mediated/WK that each constitutes less than the expected oneeighth of the instances, we develop five binary classifiers with SVM light (Joachims 1999).


In the decoding stage, the best first strategy (Ng and Cardie 2002) is used to predict bridging links.

for each a ∈ A ml , we predict the bridging link to be the most confident pair (a, c ante ) among all instances with the positive prediction.


We performed the Montague test (Morrill and Valentín 2016) on the two systems, that is, the task of providing a computational grammar of the Montague fragment.

we ran exhaustive parsing of the minicorpus given in Figure 13.


We could have also made comparison with proof net parsing/theorem proving (Moot 2016), but our proposal includes additives for which proof nets are still an open question, and not just the displacement calculus multiplicatives, and the point of focalization is that it is a general methodology extendible to, for example, exponentials and other modalities also, for which proof nets are also still under investigation.

our focalization approach is scaleable in a way that proof nets currently are not, and in this sense comparison with proof nets is not quite appropriate.


In order to save space, we will not be exhaustively showing all the cases because many follow the same pattern.

for any irreversible logical rule there are always four cases to consider corresponding to the polarity of the subformulas.


The observation by Heinz (2009), which we may repeat from the pattern formula in Example (15), is that each and every component of a regular expression describing a stress grammar should be unique.

if the neighborhood were defined as a single syllable, we would not predict to find a language that uses the same building blocks {(σσ), [(σσ)] 0 , [σ]}, but in a pattern like Example (16), where the same block is used at multiple positions in the formula: It is entirely possible for such a language to exist and be learned, but this must mean that such a learner is using a different (i.e., wider) syllable span for its definition of phonological neighborhood.


The reason, Heinz argues, is that the language learner is not able to successfully distinguish between the two possibilities (Examples (17) and (18)), and is therefore biased towards learning a grammar in which the problematic expression 'σσ' uniquely maps onto a single substring of the grammar.

the learner is intrinsically biased toward neighborhood-distinctiveness, and for this reason languages that do not obey this requirement are not learnable, and hence, nonexistent.


This criterion is coded by means of two variables: k, representing the length of the sequence of w0.s2 w0.s0 w0.s1 w0.s0 w0.s0 w0.s1 w0.s0 The result of merging states in Figure 4 with the same incoming paths of length 2. incoming edges with the same symbols for both states (describing left context of the state under consideration for merging) and l, representing the length of the sequence of outgoing edges with the same symbol for both states (describing its right context in the prefix tree).

two states are placed in the same part of the partitioning if Algorithm 2 Learning algorithm incorporating k-testable and k, l-local merging.


As an example, with k = 10 the algorithm will (obviously) fail to merge any states for any word not consisting of at least ten syllables, which will lead to a predictable failure to reconstruct the original finite-state machine in all its parsimony.

we controlled for this problematic but avoidable effect by excluding all generated data points fitting the following criteria: r the learning of the particular language with the provided settings for k and l had not succeeded; and r there were no higher values for k and l for which this case did succeed; and r there was no other case in which the learning did succeed with the provided settings for k and l. we tried to heuristically identify the maximum value, or limit, for k and l that allowed at least one language to be learned.


When k is increased beyond 3, the learnability starts to slowly but gradually decline again, which makes sense: The more context has to be identical for two states to be considered for merging, the harder it becomes to satisfy this criterion; insufficient state-merging then logically leads to a failure to (re)construct a suitably parsimonious grammar.

merging with k ≥ 3 leads to too strict merging criteria.


We arrive at this conclusion because increasing the value of l, which is the rightward context taken into account from the state under consideration for merging onward, decreases the chances of successful learningin fact, any value other than 0 turns out to be detrimental.

: The more we look to the left, the better our results turn out (up to a point, viz.


Although frequency of each word was available in the training set, we did not make use of this information.

we use only the word types (not tokens) in training.


Chapter 2 clearly describes key characteristics of time.

time can be normalized and temporal information can be organized in a hierarchical structure based on their granularities.


This is followed by a general discussion of genres or domains, covering news, Wikipedia, dialogs, short messages, and clinical reports.

this book defines a domain as a group of documents that have the same characteristics relevant for the task of temporal tagging.


Rather, his concerns were grounded in his growing belief that too many constructions found between the start of a sentence and its final punctuation didn't really belong to syntax.

aravind described parentheticals, epithets, extraposed predicates, and sentential relatives as "constructions that require a skilled tree surgeon to force a single tree over a sentence."


Structured reviews are literature reviews that are designed to be objective, repeatable, and comprehensive.

whereas the author of a normal literature review uses their knowledge of the field to identify key papers and qualitatively summarize their findings, the author of a structured review identifies relevant papers via objective search criteria and extracts from each paper key information accurate evaluation automatic human automatic measures automatic metrics automatic validity bleu correlation human empirical metrics "evaluating evaluation" improving evaluation intrinsic extrinsic meta evaluation metric human metric validity pitfalls evaluation Title search terms for identifying papers in the ACL Anthology.


Features values were weighted using the TF-IDF scheme.

the authors set out to investigate whether a more coarse-grained POS tagset would be useful for NLI.


However, recent work has drawn attention to the fact that the specific formalism for which these results were obtained, and which we will refer to as VW-CCG (after Vijay-Shanker and Weir), differs from contemporary versions of CCG in several important aspects.

it allows one to restrict and even ban the use of combinatory rules on a per-grammar basis, whereas modern CCG postulates one universal set of rules, controlled by a fully lexicalized mechanism based on typed slashes, as in other approaches to categorial grammar (Baldridge 2002;Steedman and Baldridge 2011).


For example, the two instances of backward crossed composition in Figure 3 are: Every combinatory rule over C(A) has infinitely many ground instances.

the variable X in such a rule can be replaced with infinitely many concrete categories.


It is worth mentioning here that our rules support other derivation orders than the left-branching order shown in Figure 5.

we could first combine the variable-specific categories with each other, and then combine the result with the category for c 0 .


This feature is crucial for our encoding of truth assignments.

without it we would not be able to percolate arbitrarily large truth assignments through derivation trees; our construction would work only for formulas with a bounded number of variables.


The third feature of VW-CCG that we exploit is its ability to put grammar-specific restrictions on combinatory rules.

in part 2 of our construction (Section 3.1.3), we use rules whose secondary input categories contain a mix of variables and concrete categories, such as in Equation 16: Like the availability of composition rules of unbounded degree, the ability to use rule restrictions seems to be a very powerful feature, and one that perhaps most clearly sets VW-CCG apart from TAG.


A configuration of M relative to w is a pair c = (q, α), where q ∈ Q is some state and α ∈ Σ * is a sequence of tape symbols with length |α| = m. The intended interpretation of c is that the current state of M is q, the content of the circular tape is represented by α, and the tape head is positioned to read the first symbol of α.

the initial configuration of M for w, denoted by I M (w), takes the form I M (w) = (q 0 , w# m−n ), meaning that, at the start of the computation, the machine is in the initial state, the tape consists of the n symbols of the input string w followed by m − n blanks, and the tape head is positioned to read the first symbol of w. A configuration c is called existential, universal, accepting, or rejecting based on the type of its state q.


In view of our proof of Theorem 2, we now come back to the question raised in Section 3.3.

we want to further investigate the features that are responsible for the additional complexity that comes with unrestricted VW-CCG.


The rule is also thought to be specialized for that token, meaning that the rule contributes to the derivation by introducing some structure representing the syntactic frame and valencies of the token itself.

the basic idea of our proof is to simulate valid computations of M on w through derivations of G. each fragment in a derivation of G uniquely represents some node of a valid computation.


Recall that in Section 4 we have used VW-CCG derivations to simulate moves of an ATM working with a circular tape whose size is bounded by some polynomial function of the length of the input.

we have encoded such a tape into some category X, and have used X as a primary or as a secondary input category in composition rules, in order to simulate the moves of the ATM.


The overview of the terminology in the previous sections showed that the approaches have a lot in common.

many approaches distinguish between two subclasses of abstract entities: 1.


The pronominal demonstratives this and that and the personal pronoun it are typically closer to their antecedents than this NPs (Schmid 2000;Kolhatkar 2015).

demonstrative pronouns on their own are not particularly informative, and so the distance between the anaphor and the antecedent is fairly small and the textual coherence fairly strong (i.e., there are fewer competing candidates).


(2013) and Feng and Hirst (2014), which are based on clauses as their elementary discourse units, will not always correspond smoothly with the syntactic shape or size of all non-NAs.

non-NAs may include other syntactic shapes such as verb phrases that would not be accessible in such discourse representations.


Instead, in this section, we will focus on corpora that provide useful information for building computational systems.

we will focus on the following questions.


Byron's (2003) annotation scheme is based on Schiffman's (1985) annotation scheme (see Section 4.2.1).

she used a variety of syntactic features of Schiffman's scheme: the clause level of the anaphor and the antecedent, their grammatical function, the distance between the anaphor and its antecedent, and the antecedent's syntactic category.


Antecedents could be the pronouns it, this, or that (occurring in a chain of anaphors), NPs, or verbal heads.

müller observed that annotators encountered the most difficulty in identifying NP and verbal-head antecedents: On average, all four annotators agreed on such antecedents only 6.06% of the time.


In order to do that, they started by automatically creating training data using the cataphora-like shell noun constructions from Table 3 in Section 3.1.2 and extracting the shell content using syntactic information.

they extracted instances following seven cataphora-like patterns: N-be-to, N-be-that, N-be-wh, N-to, N-that, N-wh, and N-of, and one anaphora-like pattern, Sub-be-N. Later, Kolhatkar and Hirst (2014) observed that simply using lexico-syntactic patterns creates noisy data.


ARRAU builds on other corpora, so that existing annotations can be reused.

aRRaU integrates: r a subcorpus of news texts called RST (around 230,000 tokens), which consists of the subset of the Penn Treebank that was annotated for the RST Discourse Treebank 60 (Carlson, Marcu, and Okurowski 2001).


The first study to examine shell nouns (Section 3.1.2) in a computational context, Kolhatkar and Hirst (2012), addressed anaphoric shell noun instances of this issue in the MEDLINE abstracts corpus (the annotations are described in Section 4.2.7).

they built a candidate ranking model to rank all eligible antecedent candidates of this issue in the corpus.


By contrast, the ATN parser used in the LUNAR project (Woods, Kaplan, and Nash-Webber 1972) reduced all discontinuity to local operations on registers (Thorne, Bratley, and Dewar 1968;Bobrow and Fraser 1969;Woods 1970).

unbounded wh-dependencies like the above were handled by: (a) putting a pointer into a * or HOLD register as soon as the "which" was encountered without regard to where it would end up; and (b) retrieving the pointer from HOLD when the verb needing an object "had" was encountered without regard to where it had started out.


However, it was unclear how to generalize the HOLD register to handle the multiple long-range dependencies, including crossing dependencies, that are found in many other languages.

if the HOLD register were assumed to be a stack, then the ATN becomes a two-stack machine (since we are already implicitly using one stack as a PDA to parse the context-free core grammar).


On the other hand, Generalized Phrase Structure Grammar (Gazdar 1981), Tree Adjoining Grammar (TAG; Joshi and Levy 1982), and Combinatory Categorial Grammar (CCG, Ades and Steedman, 1982) sought to reduce Move to various forms of local merger.

the latter authors suggested that the same stack could be used to capture both long-range dependency and recursion in CCG.


Nevertheless, CCG is still not as expressive as movement.

we can only capture permutations that are what is called "separable," where separability is related to the idea of obtaining the permutations by rebracketing and rotating sister nodes (Steedman 2018).


In fact, this is just another drop in the ocean of derivational ambiguity that faces all natural language processors, and can be handled by exactly the same statistical models as other varieties.

the head-word dependency models pioneered by Don Hindle, Mats Rooth, Mike Collins, and Eugene Charniak are straightforwardly applicable (Hockenmaier and Steedman 2002b;Clark and Curran 2004).


Importantly, we also evaluate (a rough indication of) the impact of each study via the citation counts of each study.

we assess whether there are observable differences in impact when comparing papers whose authors share their code directly (i.e., via a link in the paper) versus those that do not.


Though tasks such as NER and EDL have reached sufficient maturity such that commercial products are feasible, scores 3 of a top-performing system for the full knowledge base population task in TAC KBP 2016 seem disappointing (Precision 25.4; Recall 30.2; F 1 25.2).

75% of the KB triples produced are imperfect and 70% of the relations justified in text are not found.


They show experimentally that the method performs well in both settings.

in the case of deception detection, the method contributes a novel argumentative feature that, when used in combination with other features in standard supervised classifiers, outperforms the latter even on small data sets.


(3) I love that That game game is all right In this example, investment is lowered both through the evaluative predicate adjective "all right" but also through the removal of the speaking subject from the sentence.

the latter evaluation frames the utterance as an objective description of the world: "That game is all right" has the same syntax as "The sky is blue."


We cannot a priori say that a single linguistic form always indicates a specific stance, but we can find similarities among stances taken with a linguistic form containing it.

stancetaking is a composite of all parts of an utterance in its sequential context, which combine in different ways each time they are used, even as they keep a consistency of flavor across these instances.


Alignment is almost always present at a basic level in that the interlocutor must attend to the same discourse entities in order to hold a basic conversation.

speakers orient to the same things in talk.


Eckert (2000) suggests that one aspect of community of practices is indigenous norm creation.

that the norms (in this case, norms of interaction) are negotiated by members of the group coming together.


Recall that annotators were simply instructed to annotate based on how well they thought the speakers were aligning in a naive sense.

they were not instructed to annotate for ALIGNMENT based on a match of the evaluation (as for the Du Bois [2007] stance model), although that might have been something that speakers keyed into.


The second part of the post (009-2) is simply some explanation of the mechanism of nuclear weapons, and the neutral score it gets for ALIGNMENT shows that it is neutrally aligning.

it is neither disaligning nor overtly marking some alignment.


For our work we consider the social perspective of stance.

we follow the definition of Kiesling (2009), which is based on the stance triangle approach of Du Bois (2007).


Ochs (1992) has argued that social identity categories are mediated by stance; she argues that stances (along with acts and activities) are the notions more likely to be what speakers are orienting to in an actual interaction.

rather than choosing a linguistic form because it is masculine, a speaker might choose it because, for example, it is low investment, and such low investment stances are constitutive of masculinity.


To exploit discourse structure of microblog conversations, we link microblog posts using reposting and replying relations to build conversation trees.

the root of a conversation tree refers to the original post and its edges represent the reposting or replying relations.


Previous studies have shown that the topic representation captured by topic models is useful for summarization (Nenkova and McKeown 2012).

there are two different purposes of using topic models in existing summarization systems: (1) to separate summary-worthy content and noncontent background (general information) (Daumé and Marcu 2006;Haghighi and Vanderwende 2009; Ç elikyilmaz and Hakkani-Tür 2010), and (2) to cluster sentences or documents into topics, with summaries then generated from each topic cluster for minimizing content redundancy (Salton et al.


According to its assumption, a coherent document can be represented by text units at different levels (e.g., clauses, sentences, paragraphs) in a hierarchical tree structure.

the minimal units in RST (i.e., leaves of the tree structure) are defined as sub-sentential clauses, namely, elementary discourse units.


Assuming that the index of m's parent is pa(m), we use transition probabilities π d ∼ Dir is the discourse assignment on pa(m).

to create a unified generation story, we place a pseudo message emitting no word before the root of each conversation tree and assign dummy discourse indexing D + 1 to it.


For instance, a statement message (e.g., [R3] in Figure 1) may contain more content words for topic representation than a question to other users (e.g., [R1] in Figure 1).

stop words and punctuation are forced to be labeled as discourse or background words.


26 In implementation, as globally optimizing Equation (2) is exponential in the total number of messages in a conversation, which is a non-deterministic polynomial-time (NP) problem, we use the greedy approximation adopted in Haghighi and Vanderwende (2009) for local optimization.

messages are greedily added to a summary so long as they minimize the KL-divergence in the current step.


We quantitatively evaluate the performance of summarizers using ROUGE scores (Lin 2004) as a benchmark, a widely used standard for automatic summarization evaluation based on the overlapping units between a produced summary and a gold-standard reference.

table 7 reports ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-SU4 output by ROUGE 1.5.5.


This observation demonstrates that microblog conversation summarization is essentially challenging, where manual annotations (although with high cost in time and efforts involved) can provide useful clues in guiding systems to produce summaries that will be liked by humans.


Before providing the formula of sampling steps, we first define the notations of all variables used in the formulations of Gibbs sampling, described in Table A.1.

the various C variables refer to counts excluding the message m on conversation tree c.


In this architecture, two separate LSTMs are used-LSTM pt and LSTM ct -similar to the previous architecture without any attention, but for LSTM ct , its memory state is initialized with the last cell state of LSTM pt .

lSTM ct is conditioned on the representation of the lSTM pt that is built on the prior turn pt.


This incongruity might become diffuse if the inputs are combined too soon, and we have shown that the multiple-LSTM architecture outperforms a single LSTM architecture that combines the current turn and the context.

lSTM networks with sentence-level attention achieved significant improvement when using prior turn as context for all the data sets (e.g., 6-11 percentage point F1 for IAC v2 and Twitter messages).


From the results in Table 6, we can deduce that combining feature types improves classification performance, given that more than half of the combinations present an improvement over the character n-gram baseline and lexical features alone.

combining lexical with semantic and syntactic features seems to work well for irony detection, yielding a top F 1 score of 70.11%.


We show experimentally that our method performs well in both settings.

in the case of deception detection, our method contributes a novel argumentative feature that, when used in combination with other features in standard supervised classifiers, outperforms the latter even on small data sets.


Classification of pairs of sentences, amounting to identifying relations between texts, has recently received a great deal of attention.

in this article we focus on the RbAM task as defined by Carstens and Toni (2015), which aims to automatically identify relations between arguments to create BAFs (Cayrol and Lagasquie-Schiex 2005).


After the merge layer, we also add a dense feedforward layer to test whether its inclusion has an impact on the results.

we conducted experiments to test the influence of a dense feedforward layer before softmax to determine the type of class from L. We run experiments using the same architecture with BiLSTMs as well as (unidirectional) LSTMs.


The classification model based on RFs uses the features shown in Table 6.

for the combined semantic and syntactic feature, we use two similarity measures between words: path represents the shortest path that connects the senses in the is-a (hypernym/hypnoym) taxonomy and lch represents the Leacock-Chodorow similarity, namely, the shortest path between the senses divided by double the maximum depth in the taxonomy in which the senses occur.


Further, we plan to experiment with other deep learning architectures for RbAM.

inspired by the demonstrated effectiveness of attention-based models (Yang et al.


Among methods that use unsupervised learning, Ritter, Cherry, and Dolan (2010) propose two HMM-based unsupervised conversational models for modeling speech acts in Twitter.

they use a simple HMM and a HMM+Topic model to cluster the Twitter posts (not the sentences) into act types.


However, as we will see, although simple concatenation of data sets generally improves the performance of the LSTM-RNN model, it does not provide the optimal solution because the conversations in synchronous and asynchronous domains are different in modality (spoken vs. written) and in style.

to get the best out of the available synchronous domain data, we need to adapt our model.


When backpropagating to the generator network, they consider a slight variation of the reverse gradients with respect to the discriminator loss.

ifd ω is the discriminator probability for real images, rather than reversing the gradients of − log(1 −d ω ), they backpropagate the gradients of − logd ω to the generator.


(2016, page 21), who initialize λ to 0, and then change it gradually to 1 as training progresses.

we start training the task classifier first, and we gradually add the discriminator's loss.


We modify the training algorithm accordingly.

each minibatch in SGD training is formed by labeled instances from both D S and D l T , and unlabeled instances from D u T .


It can be observed that in general the compositional methods perform better than the non-compositional ones (e.g., averaging, concatenation), and when the compositional method is trained on the task, we get the best performance on two out of three data sets.

our B-LSTM conv-glove gets the best results on QC3 and TA, outperforming B-GRU conv-glove by a slight margin in F 1 .


Further experiments with CRFs show that global joint models improve over local models given that the models consider the right graph structure.

the LC-FC 1 and FC-FC graph structures were among the best performers.


In addition, if the instructions are clear, σ can also be seen as an indication of the level of difficulty of the task.

all other things being equal, compounds with larger σ can be considered intrinsically harder to analyze by the participants.


Thus, most head and modifier scores (hc H and hc M ) are close to or above the diagonal line in Figure 1(b).

a component of the compound is seldom rated as less literal than the compositionality of the whole compound hc HM , although the opposite is more common.


These combinations produce a total of 228 DSMs per language (see Table 3).

we evaluate the influence of the following parameters on compositionality prediction: • WINDOWSIZE: Number of context words to the left/right of the target word when searching for target-context co-occurrence pairs.


For each parameter, we analyze all possible values of other parameters.

we report the best results obtained by fixing a value and considering all possible configurations of other parameters.


As this may have an impact on the success of compositionality prediction, in this section we evaluate how different compositionality prediction functions model these compounds.

we proposed pc maxsim , (Section 4) for dynamically determining weights that assign maximal similarity between the compound and each of its components.


The ultimate test would be to use predicted compositionality scores in downstream applications and tasks involving some degree of semantic processing, ranging from MWE identification to parsing, and word-sense disambiguation.

it would be interesting to predict compositionality in context, in order to distinguish idiomatic from literal usages in sentences.


47 The results unanimously agree that a higher threshold does not contribute to the removal of any extra noise.

for PPMI-SVD, it seems to discard enough useful information to considerably reduce the quality of the compositionality prediction measure.


Along with the verification of parameters, we also evaluate whether data set variations could yield better results.

we consider the use of filtering techniques, which are used in the literature as a method of guaranteeing data set quality.


For this reason, we can also write this equation as log l∈L(c) p(l|x), where L(c) is the set of consistent logical forms that execute to the correct denotation.

p(l|x) is computed with a log-linear model: where L is the set of candidate logical forms; φ is the feature function that maps an utterance-logical form pair (and also the corresponding denotation) into a feature vector; and θ denotes the weight parameter of the model.


2016) contains 93,319 questions derived from CLUEWEB09 (Gabrilovich, Ringgaard, and Subramanya 2013) sentences.

the questions were created by randomly removing an entity, thus producing sentence-denotation pairs (Reddy, Lapata, and Steedman 2014).


The first block contains conventional statistical semantic parsers, previously proposed neural models are presented in the second block, and variants of TNSP are shown in the third block.

we build various top-down and bottom-up TNSP models using the various types of attention introduced in Section 3.7.


In recent years, parsing a sentence to a dependency representation has been well studied and widely applied to many Natural Language Processing (NLP) tasks, for example, Information Extraction and Machine Translation.

the data-driven approaches have made great progress during the past two decades (Kübler, McDonald, and Nivre 2009;Pitler, Kannan, and Marcus 2013;Chen and Manning 2014;Weiss et al.


To enhance the tree-shaped dependency representation, we borrow the key ideas underlying Lexical Function Grammar (LFG) (Bresnan and Kaplan [1982], Dalrymple [2001]), a well-defined and widely-applied linguistic grammar formalism.

gRs  An example: Pudong recently enacted regulatory documents involving the economic field.


To deal with this problem, we propose modeling the graph decomposition problem as a constrained optimization problem and leverage appropriate objective functions as well as constraints to reflect essential structure-specific properties of the syntactically-motivated GR graphs.

we consider the reachability property: In a given GR graph, every node is reachable from the same unique root.


Detailed analysis reveal some important factors that may possibly boost the performance.

the following non-obvious facts should be highlighted: 1.


This approach was pioneered by Yamada and Matsumoto (2003) and Nivre, Hall, and Nilsson (2004).

deep learning techniques have been successfully applied to enhance the parsing accuracy (Chen and Manning 2014;Weiss et al.


Kiperwasser and Goldberg (2016) proposed a simple yet effective architecture to implement neural dependency parsers.

a bidirectional-LSTM (Bi-LSTM) is utilized as a powerful feature extractor to assist a dependency parser.


(2011) and Tse and Curran (2012) analyze the challenges and difficulties in Chinese deep parsing.

some language-specific properties account for a large number of errors.


We take into account both theoretical assumptions and analyzing practices to enrich co-indexation information according to phrase structure patterns.

we try to link an anaphoric empty category e with its c-commonders if no non-empty antecedent has already been co-indexed with e. Because the CTB is influenced deeply by the X-bar syntax, which highly regulates constituent analysis, the number of linguistic rules we have is quite modest.


Linguistic experts carefully examine the corresponding GR graphs derived by our extraction algorithm and correct all errors.

a gold-standard GR annotation set is created.


The reasoning is that we use one tree to capture long distance information and the other two to capture coordination information.

7 we decompose each given graph y y y into three tree-like subgraphs g g g 1 , g g g 2 , and g g g 3 for each subgraph to carry important information of the graph as well as cover all edges in y y y.


With this property, each individual problem g g g k ← arg max g g g k f k (g g g k ) + u A k g g g k can be decoded easily, with modifications to the firstorder weights of the edges in the three models.

let w k = u A k , then we can modify the ω k in s k to ω k , such that ω k (i, j, t) = ω k (i, j, t) + w k (i, j, t) + w k (j, i, t).


w n and label set R the following holds: The vertex −1 denotes a virtual root.

the arc set A represents the labeled dependency relations of the particular analysis G. an arc (w i , w j , r) ∈ A represents a dependency relation from head w i to dependent w j labeled with relation type r. A dependency graph G is thus a set of labeled dependency relations between the words of x.


The key design of ELMo is to train an LSTM-based language model and utilize the hidden vector as contextualizd word embeddings.

every word is assigned a vector according to information on the whole sentence rather than several neighboring words in a limited context.


The framework view allows us to tackle the challenges of context-response matching from a high level.

sMF matches each utterance in the context with the response at the first step and forms a sequence of matching vectors.


Then, we propose a new model named sequential attention network (SAN), which implements the utterance-response matching layer of SMF with an attention mechanism.

for an utterance-response pair, SAN lets the response attend to important parts (either words or segments) in the utterance by weighting the parts using each part of the response.


Experimental results show that on both data sets, both SMN and SAN can significantly outperform the existing methods.

on the Ubuntu corpus, SMN and SAN yield 6 and 7 percentage point improvement, respectively, on R 10 @1 over the best performing baseline method, and on the Douban corpus, the improvement on mean average precision from SMN and SAN over the best baseline are 2.6 and 3.6 percentage points, respectively.


The extension in this article includes a unified framework for the existing methods, a proposal of a new framework for context-response matching, and a new model under the framework.

the contributions of this work include the following.


With U and R, SCN constructs a word-word similarity matrix M 1 ∈ R n u ×n r and a sequence-sequence similarity matrix M 2 ∈ R n u ×n r as two input channels of a convolutional neural network (CNN).

the CNN then extracts important matching information from the two matrices and encodes the information into a matching vector v. ∀i, j, the (i, j)-th element of M 1 is defined by M 1 models the interaction between u and r on a word level.


The interactions are finally aggregated following the order of the parts in the response as a matching vector.

∀e r,i ∈ R, the weight of e u,j ∈ U is given by where W att1 ∈ R d×d , and b att1 ∈ R are parameters.


Most Greenbergian universals are implicational, that is, given in the form of "if x holds, then y also holds."

the combination of (x, ¬y) is non-existent (absolute universals) or rare (statistical universals).


However, if we give up theoretical rigorousness, it is not difficult to draw samples from the posterior, which are only approximately correct but work well in practice.

we use the double MH sampler (Liang 2010).


Linguistic parameters are sorted by u k in decreasing order.

a linguistic parameter with a smaller index is more likely to be on.


In this work, we present a novel contribution to analyze and automatically classify event mentions in historical documents.

we develop annotation guidelines and an annotated corpus for the domain, and then we present some experiments for the automatic detection and classification of event mentions in historical texts.


Both these corpora were annotated following the TimeML guidelines without any adaptation to the history domain.

no attempt was made to find a domain-specific definition of event, unlike what happened in other domains, such as the clinical one.


5 In HTOED a distinction is made between categories connected with a physical existence and those having a social dimension: Due to this subtle difference an event of movement can belong to the TRAVEL AND TRAVELING, the SPACE, or the MOVEMENT category.

discerning between physical and social dimensions is ambiguous.


The Dice coefficient (Dice 1945) is given for the identification of event mentions, distinguishing between  the agreement calculated on extensions perfectly detected by both annotators and the one measured on the number of annotated tokens shared by both annotators, thus considering also a partial match.

with the Dice Coefficient we measure the agreement in determining whether each token is part or not of an event mention.


This latter task implies also the identification of mentions from raw text.

no golden event mentions are given in input to the system.


Although other, more semantically rich features could be used, we limit our feature set to a few basic ones, in line with the setting adopted with the BiLSTM approach (Section 5.3), which relies only on word embeddings.

in the next subsection we present the results of several experiments carried out on the development set: we analyze the impact of the features and of the size of the context window on the performance of the classifiers.


Both approaches failed to classify events of the classes FOOD-FARMING and FAITH, which had very few occurrences in both the training and the test set.

the BiLStM model wrongly classified EDUCAtION events: it assigned the class MENtAL-ABStRACt to the verb "to learn."


In this work, we provided a theoretical and practical investigation on the topic of event detection and classification in historical texts.

we presented new annotation guidelines for event mention detection and classification, and then a new manually annotated corpus of historical texts, the largest publicly available to address the task in this domain.


Thus T[O] would transduce an input 4 to <ord>fourth</ord>; these tags will be used subsequently to induce general class rules from examples involving particular class instances.

we can then define a mapper from the markup to the tagged representation as: we define the Map as the concatenative closure of the taggers for each of the known types.


Each node n ∈ N is associated with a set Bag(n) ⊆ V, referred to as a bag, in such a way that the following properties are all satisfied.

vertex cover: The nodes of the tree T cover all the vertices of G: Edge cover: Each edge in G is included in some node of T. for all edges (u, v) ∈ E, there exists an n ∈ N with u, v ∈ Bag(n).


Vertex cover: The nodes of the tree T cover all the vertices of G: Edge cover: Each edge in G is included in some node of T. That is, for all edges (u, v) ∈ E, there exists an n ∈ N with u, v ∈ Bag(n).

running intersection: The nodes of T containing a given vertex of G form a connected subtree of T. for all n, n 1 , The width of a tree decomposition T is max n∈N |bag(n)| − 1.


This span assignment satisfies the properties of the outside parse trees we use for outside tree decompositions.

for every node To see this, we observe that H(T 4 , u) cannot be dominated by , then there would be no node n in T 4 such that u, v ∈ Bag(n).


According to the tree decomposition in Figure 6, vertex v is anchored at r and the valence constraint for v is determined at r itself.

v needs an outgoing edge with label d and an incoming edge with label c, which are dispatched to the first and to the second nonterminals, respectively, in r's right-hand side.


In order to process binary rules r : A → BC, we need to account for the spans of the nonterminals B and C, which depend on whether r is anchored or not.

if r is anchored we need to introduce a vertex in between B and C. We use a special term α(r) = 1 for anchored r and α(r) = 0 for unanchored r. Rule r is then processed by the nested loops at line 9, which use index k and α(r) to indicate the boundary between the spans of B and C. We choose a mapping φ r from r's vertices to the input vertices, with the proviso that if r is anchored then r's anchored vertex is mapped to v k .


In what way do different language representations encode language similarities?

is genetic similarity what is really captured?


(2018), unlike traditional word type embeddings, each token is assigned a representation that is a function of the entire input sentence.

the authors use vectors derived from a bidirectional long short-term memory network (LSTM) that is trained with a coupled language model in order to build contextualized vectors.


We induce node senses using the word neighborhood clustering approach by Dorow and Widdows (2003).

we assume that the removal of the nodes participating in many triangles separates a graph into several Algorithm 1 WATSET, a Local-Global Meta-Algorithm for Fuzzy Graph Clustering.


The resource is constructed using crowdsourcing and mostly covers general vocabulary.

non-expert users are allowed to edit synsets in a collaborative way, loosely supervised by a team of project curators.


Input: a set of SVO triples T ⊆  Construct edges using nearest neighbors 4: Cluster the graph 7: distributional representations of words.

we use a word embedding model to map every triple t = (s, p, o) ∈ T to a (3d)-dimensional vector t = s ⊕ p ⊕ o (lines 1-2).


In addition to the experiments based on gold standard lexical resources, we also performed a manual evaluation.

we assessed the quality of the frames produced by the Triframes WATSET[CW top , CW top ] approach using n = 30 nearest neighbors for constructing a triple graph, which showed the best performance during automatic evaluation (Tables 15 and 16).


The following sections will provide details of this experiment.

section 6.1 presents two data sets that are used as gold standard clustering in the experiments.


We study whether five scaling properties (given by Zipf's law, Heaps' law, Ebeling's method, Taylor's law, and longrange correlation analysis) can serve for evaluation of computational models.

we test n-gram language models, a probabilistic context-free grammar, language models based on Simon/Pitman-Yor processes, neural language models, and generative adversarial networks for text generation.


This refers to the way that sequences of characters or words in natural language universally exhibit clustering, bursty behavior.

results using Taylor's law (Kobayashi and Tanaka-Ishii 2018;Tanaka-Ishii and Kobayashi 2018) show that a natural language text has a consistent range for the Taylor exponent, which quantifies the degree of burstiness in the text.


In §4, we provide a summary of the models of natural language considered in this article.

our work covers n-gram language models, mathematical language models based on the Simon and Pitman-Yor processes, grammatical models, and neural language models.


They therefore serve to evaluate whether a language model has the same behavior as natural language text.

given a text generated by a language model, we set two levels of assessment for evaluation: Q1 Does the scaling property hold qualitatively?


Other than the original n-gram model, we also test models with a variety of smoothing techniques to improve the perplexity.

linear interpolation (Stolcke 2002), Katz backoff (Katz 1987), and Kneser-Ney smoothing (Kneser and Ney 1995) have been known to enhance the performance of n-gram models.


We expected models that learned a natural language text to be able to generate a sample text with scaling properties resembling those of the original text.

we expected that the exponent values would be close to those of the original data set.


The RNNs with a gating mechanism improved the perplexity over that of the simple RNN model.

the AWD-LSTM model performed the best among the RNN language models.


Compared with those methods, the Taylor exponent ζ had a clearer correlation with the perplexity of the models.

the exponent satisfied ζ = 0.50 for all n-gram language models.


Because of the data set used, the GAN models are limited to generating a certain text type (i.e., image captions).

as the length of the text is short, the results are readily expected not to reproduce long memory behavior.


In order to carry out this study, we borrow methods from finance and econometrics that-to the best of our knowledge-have not been applied in natural language processing.

we use a local dependency measure recently proposed by Tjøstheim and Hufthammer (2013) to describe the relation between metric scores and human judgments at different levels of translation quality.


Measures the similarity between MT and the reference translation based on the number of matching word n-grams.

bLEU score is a product between n-gram precision and a brevity penalty that down-scales the score for the MT outputs that are shorter in length than the reference translation.


Dependency trees are obtained using MINIPAR (Lin 2003).

the following metrics are defined: • DP-HWCM: Head-Word Chain Matching (Liu and Gildea 2005 Màrquez 2010a, 2010b) analyze similarities between MT outputs and reference translations by comparing the semantic roles (SRs) (i.e., arguments and adjuncts) that occur in them.


To estimate θ(x), Tjøstheim and Hufthammer (2013) follow the local likelihood approach proposed by Hjort and Jones (1996).

they define the parameters θ(x) to be the minimizer of the following local penalty function: (3) where K b is a product kernel function with bandwidth b = (b 1 , b 2 ).


(2013) to compare evaluation consistency.

using system-level data from the WMT16 Metrics Task, we computed the average difference between the scores assigned to the same MT output by different judges and by the same judge.


Although different criteria are used in manual evaluation, the results for different data sets are very closely related.

wMT16, GALE, and MTC-A-where human evaluation is based on adequacy-behave very similarly.


We advocate for a new approach that adapts the broad and discrete nature of typological categories to the contextual and continuous nature of machine learning algorithms used in contemporary NLP.

we suggest that such an approach could be facilitated by recent developments in data-driven induction of typological knowledge.


Similarities between languages do not always arise from language-internal dynamics but also from external factors.

similarities can be inherited from a common ancestor (genealogical bias) or borrowed by contact with a neighbor (areal bias) (Bakker 2010).


Each language can adopt several strategies for a given construction and partly inconsistent semantic categories.

typological patterns tend to be gradient.


Bybee and McClelland (2005) have noted how this gradience resembles the patterns learned by connectionist networks (and statistical machine learning algorithms in general).

they argue that such architectures are sensitive to both local (contextual) information and general patterns, as well as to their frequency of use, similarly to natural languages.


This class of methods infers probabilistic models of the observations given some latent variables.

it unravels the hidden structures within unlabeled text data.


Typological information offers promising ways to address this problem.

we have noted that it can support model transfer, parameter sharing, and input biasing through language vectors.


In the next two sections, we elaborate on these solutions.

we review the development of typological information and the specific features that are selected for various NLP tasks ( § 4).


Other shortcomings are related to feature granularity.

most databases fail to account for feature value variation within each language: They report only majority value rather than the full range of possible values and their corresponding frequencies.


Finally, a small number of experiments adopted the entire feature inventory of typological databases, without any sort of pre-selection.


Each row represents a language in the data; each cell is colored according to the feature value, ranging from 0 to 1.

the feature set of Figure 5 In order to compare the similarities of the typological feature vectors among languages, we clustered languages hierarchically based on such vectors.


These language vectors are trained end-to-end as part of neural models for downstream tasks such as many-to-one Neural Machine Translation (NMT).

language vectors can be obtained from artificial trainable tokens concatenated to every input sentence, similar to Johnson et al.


However, according to Murawaki (2017), these interactions have different degrees of impact on typological features.

inter-feature dependencies are more influential than inter-language dependencies, and horizontal diffusibility (borrowing from neighbors) is more prominent than vertical stability (inheriting from ancestors).


The typological features developed as discussed in § 4 are of significant importance for NLP algorithms.


Täckström, McDonald, and Nivre (2013) proposed a discriminative version of the model, in order to amend the alleged limitations of the original generative variant.

they dispose of the strong independence assumptions (e.g., between choice and ordering of modifiers) and invalid feature combinations.


The prior knowledge from the typological database enables the model to forbid the invalid interactions, by generating intermediate feature embeddings in a hierarchical structure.

given n words and l dependency relations, each arc h → m is encoded as the tensor product of three feature vectors for heads Φ h ∈ R n , modifiers Φ m ∈ R n , and the arcs Φ h→m ∈ R l .


The language embeddings consist of (a non-linear transformation of) either a mere one-hot identity vector or a vector of typological properties taken from WALS.

they are added to both input feature and action vectors, to affect the three above-mentioned modules individually, and concatenated to the hidden module representations, to affect the entire parser state.


(2018a) leverage typological features to pre-process treebanks in order to reduce their variation in language transfer tasks.

they adapt source trees to the typology of a target language with respect to several constructions in a rule-based fashion.


Based on the results in Figure 10, it is unclear whether typology should be preferred, as it is sometimes rivaled by other types of features.

it is typology that excels according to Tsvetkov et al.


In this section we discuss potential future research avenues that may result in a closer and more effective integration of linguistic typology and multilingual NLP.

we discuss: (1) the extension of existing methods to new tasks, possibly exploiting typological resources that have been neglected thus far ( § 6.1); (2) new methods for injecting typological information into NLP models as soft constraints or auxiliary objectives ( § 6.2); and (3) new ways to acquire and represent typological information that reflect the gradient and contextual nature of cross-lingual variation ( § 6.3).


Several techniques surveyed in § 4.3 are suited to serve this purpose.

the extraction from morphosyntactic annotation (Liu 2010, inter alia) and alignments from multi-parallel texts (Asgari and Schütze 2017, inter alia) provide information about typological constructions at the level of individual examples.


As an alternative, selective sharing and multilingual biasing could be performed at the level of individual examples rather than languages as a whole.

model parameters could be transferred among similar examples; and input/hidden representations could be conditioned on contextual typological patterns.


A key question for research is: Are these text formatting features useful for NLP tasks?

in this article, we will try to identify whether these text formatting features are useful for information extraction.


Hence, we use a greedy procedure, beam search, with a fixed beam size (10) for inference.

in each step, we only expand the ten most promising candidates so far given by the current score.


Most of these features depend on typographical information that is vital in performance of the axiom identification component as well as the overall model.

we can observe that the axiom, theorem, corollary annotation, and bounding box features contribute most to the performance of the model as they are direct indicators of the presence of an axiom mention.


Note that these features depend on typographical information that is again vital in performance.

we can observe that the overlap and the XML structure features contribute most to the performance of the model.


The axiom parsing component uses a few content-based features, such as span similarity and number of relations, span lengths, and relative position; and various discourse features, such as discourse markers, punctuations, text organization, RST parse, an existing discourse segmentor from Soricut and Marcu (Soricut and Marcu 2003), node attachment, syntax, dominance, and XML structure; and all are clearly important.

we can observe that span similarity and punctuation features contribute most to the performance of the model.


First, we address the task of identifying cognates.

given a pair of words (u, v), we determine whether they are cognates or not.


Second, we investigate the task of discriminating between cognates and borrowings.

given a pair of words (u, v), we determine whether they are cognates or v is the etymon of u.


Further, we extend the research problem adding a new class for unrelated words.

we have a classification task with three classes: cognates, borrowings, and unrelated words.


We favor words that occur on the first position in multiple n-best lists.

for a given n-best list, we associate weights in a de Borda (1781) sense to all the words from the list: we give weight n to the word produced with the highest confidence, n − 1 to the second one, and so on, until weight 1 to the n-th produced word.


We give more importance to the vote of the languages that obtained a better performance, multiplying the weight by the training accuracy.

for each n-best list L i , let π(i) be the training accuracy for language i.


We use a "majority class" type of baseline that does not take context into account.

it replaces each character in the source word with the most probably corresponding character in the target word, without taking previous and subsequent characters into account.


Both lemmatization and stemming reduce the form of inflected or derived words to a common base word form, but stemming does this in a more drastic manner.

it reduces the words to much shorter forms than lemmatization.


The transferability is built on an auxiliary task and a conditional domain adversarial network to reduce domain distribution difference in the hidden spaces effectively in word level through syntactic relations.

the auxiliary task builds structural correspondences Submission Fine-grained opinion mining (Pang and Lee 2008; Liu 2011) aims to extract important information from opinionated texts.


The second module is a conditional domain adversarial network for projecting the representations of syntactically-similar words into a common space.

tRNN is built on a recursive structure shown in the right-hand side of Figure 2.


To address this issue, a domain discriminator D(•; θ d ) is incorporated in the learning process.

when feeding an instance x either from the source domain or target domain, the model will generate the following two outputs: with y and d indicating the main prediction and domain prediction, respectively.


(2018) proposed a conditional domain adversarial network.

the domain discriminator is applied on the joint distribution of data and classes, instead of only on the input data.


An example of the model structure is shown in Figure 3.

the network takes word embedding x n for each word as the input and produces the hidden vector h n as the output.


Although sharing relational transformation matrices aligns with the fact that dependency relations are domain-invariant, this cannot guarantee learning of domain-invariant hidden representations.

when the two aspect words from two domains have disjoint features and children according to the dependency trees, the resultant hidden vectors are also dissimilar.


Meanwhile, knowledge transfer takes place via two transferable components: an auxiliary task as well as a conditional domain adversarial network are adopted to learn domain-invariant representations.

given a dependency path, TRNN first produces a relation vector r ij through its connected words.


Instead of using child node features as in SRNN, we incorporate a dependency relation vector to produce the hidden representation for the parent nodes.

we begin with input word embedding x i ∈ R D for each node.


2018, we adopt conditional cDANs that condition the domain discriminator on the dependency structure of a sentence.

the domain discriminator D generates a probability distribution P(D|h, y G ) as already shown in Figure 4, where y G refers to the relation label feature.


Compared with the auxiliary task, which focuses on learning transferable word features based on the downward dependency relations, cDAN works in the other direction and further improves the transferability of the model when combining with the auxiliary module.

these two transferable modules aim at learning syntactically sensitive word representations that are transferable across domains.


To reduce the negative effect of noisy relations on cDAN, we replace the explicit one-hot relation feature with relation cluster labels.

y G mi in Equation (5) becomes a one-hot vector with y G mi [k] = 1, where k indicates the index of the relation group it belongs to.


we denote θ f , θ R , θ d , and θ y as parameters for feature learning, auxiliary relation prediction task, domain discriminator, and final prediction task, respectively.


To make robust comparisons, we conduct each experiment for three times and take the average performance as the final result.

we make three random splits to partition the data in each domain into a training set and a testing set with the proportion being 3:1.


All the hyper-parameters for cross-domain experiments are selected based on three-round random-split validation on the extraction task in the source domain and relation prediction task over both source and target domain.

for source-domain validation data, we evaluate the model's performance on both terms, extraction task and relation prediction task.


The last four rows in Table 2 are variations of the proposed model.

cRF-embedding removes RNN for syntactic modeling, whereas SRNN removes the sequential layer on top.


Besides the pairwise transfer setting, we also conduct multisource adaptation experiments.

we select two different domains as source domains with labeled training data and the third domain as the target domain.


To clarify that, we construct another data set to simulate noisy dependency relations that are common for informal texts.

in the source domain of each transfer experiment, for each relation that connects to any aspect or opinion word, it has 0.5 probability of being replaced by any other relation.


We use a measure of domain distance to evaluate the effect of knowledge transfer.

the maximum mean discrepancy (MMD) (Gretton et al.


Because the model works on word-level predictions that treat each word as an instance, we take the average word features for each sentence as the input to compute the domain distance and use a mixed RBF kernel for the MMD mapping.

the mixed RBF kernel consists of three kernels with different kernel widths: 0.5, 0.025, and 0.0125.


Word Dependency 1 this, the, their, my, here, it, I, our, not det, poss, neg 2 quality, jukebox, maitre-d, sauces, portions, volume, friend, noodles, calamari dobj, iobj, pobj 3 in, slightly, often, overall, regularly, since, back, much, ago prep, mark, advmod 4 handy, tastier, white, salty, right, vibrant, first, ok amod, advmod 5 get, went, impressed, had, try, said, recommended, call, love ccomp, pcomp, xcomp 6 is, are, feels, believes, seems, like, will, would cop, auxpass To qualitatively show the effect of the auto-encoders for clustering syntactically similar words across domains, we provide some case studies on the predicted groups of selected words in Table 6.

for each relation in the dependency tree, we use Equation 8to obtain the most probable group to assign the word in the child node.


Figures 8 and 9 provide the performance variations by changing the hyperparameters.

figure 8 provides the average performance over six source-target experiments for varying β and the number of clustered relation group |G|.


Furthermore, the ability of TRNN-GRU for knowledge transfer can be qualitatively shown in Figure 10.

we compare the results of TRNN-GRU with baseline model Hier-Joint on the performances with different proportions of unlabeled target training data from 0 to 1.


Rather, the regular compatibility of case specifications between the involved syntactic items is due to a modification relation realised in a governed environment.

we can regard concordial case as a typical instance of a governed modification.


there may be a number of alternatives both for the segmentation of a form into a stem and an inflectional ending and for the assignment of a stem to a lexeme.


C-structure accuracy on the Homecentre The results indicate that LFG-DOP's functional structures help to improve the parse accuracy of treestructures.

lFG-DOP outperforms Tree-DOP if evaluated on tree-structures only.


In that case the model backs off to a frequency ordering of the subtrees.

all subtrees of each root label are assigned a rank according to their frequency in the corpus: the most frequent subtree (or subtrees) of each root label get rank 1, the second most frequent subtree gets rank 2, etc.


Segmentation is a document analysis function which directly exploits one of the core text cohesion factors, patterns of lexical repetition (see Section 1.1), for identifying some baseline data concerning the distribution of topics in a text.

discourse segmentation is driven by the determination of points in the narrative where perceptible discontinuities in the text cohesion are detected.


Following the original idea of lexical chains (Morris and Hirst, 1991), subsequently developed specifically for the purposes of segmentation of expository text (Hearst, 1994), we have adapted an algorithm for discourse segmentation to our document processing environment.

while remaining sensitive to the distribution of "terms" across the document, and calculating similarity between adjacent text blocks by a cosine measure, our procedure differs from that in (Hearst, 1994) in several ways.


For example the classifier -nin adds the restriction y:human.

it can only be used to classify human referents.


5 Hypothesis (1) seems plausible, since the word alignment system used (Tiedemann (to appear a), Tiedemann (to appear b)) actually already utilizes several kinds of information to align the words in the two texts.

it uses distributional information, cooccurrence statistics, iterative size reduction, 'naive' stemming, and string similarity to select and rank word alignment candidates (but not linear order; cf.


It certainly seemed that the closer genetic relatedness of the two Slavic languages worked to our advantage, but we concluded that we needed to do more experiments, both with more language combinations and with a modified sampling procedure.

we wanted to get rid of the problematic function words (see above).


The key idea is similar at these two steps.

predicate argument structure forms the kernel of a sentence, thus verbs and nouns are regarded as important cues for similarity measures.


Those MUs that are similar in a specific event are common focuses of different reporters.


When detecting if there is a table in free text, we should disambiguate the uses of the special symbols.

the special symbol may be a separator or content of cells.


Two filtering rules are employed to disambiguate their functions: (1) A table must contain at least two cells to represent attribute and value.

the structure with only one cell is filtered out.


(2) Otherwise, we start the similarity checking from the right-bottom position, i.e., c nm .

the n th row and the m th column are regarded as base for comparisons.


(5) If the count is larger than (n-2)/2, and the similarity of row 1 and row n is smaller than the similarity of the other row pairs, then we say this table can be read in column wise.


Our empirical experiments confirm the intuition that positive adaptation, Pr( + adapt), is typically much larger than negative adaptation, Pr( − adapt).

pr( + adapt) >> pr(prior) > pr( − adapt).


A similar pattern holds for negative adaptation, but in the reverse direction.

pr( − adapt)/ pr(prior) is much smaller for ''Kennedy'' than for ''except.''


Intuitively, there should still be some adaptation.

the probability of ''peace'' should go up quite a bit (positive adaptation), and the probability of many other words such as ''piece'' should go down a little (negative adaptation).


In estimating adaptation probabilities, we continue to use a, The table below shows that ''Kennedy'' adapts more than ''except'' and that ''peace'' adapts more than ''piece.''

''Kennedy'' has a larger spread than ''except'' between the history and the otherwise case.


Since it is likely that both linear-and discourse-based anaphora resolution systems can implement similar FILTER and PREFERENCE strategies, we focus here only on the strategies that can be used to COL-LECT lists of potential antecedents.

we focus on determining whether discourse theories can help an anaphora resolution system determine LPAs that are "better" than the LPAs that can be computed from a linear interpretation of texts.


By examining the potential of each model to correctly determine co-referential expressions for each k, it is possible to determine the degree to which an implementation of a given approach can contribute to the overall efficiency of anaphora resolution systems.

if a given model has the potential to correctly determine a significant percentage of co-referential expressions with small DRAs, an anaphora resolution system implementing that model will have to consider fewer options overall.


The second limitation comes from the fact that the current styling tools for rendering an XML document, such as CSS (Cascading Style Sheets), which are a strictly layout-oriented language, or XSLT (XSL transformation language), which is a more generic tool for transforming an XML document into another one (such as a displayoriented HTML file) are poorly adapted to linguistic processing.

it seems difficult in such formalisms to express such basic grammatical facts as number or gender agreement.


The expression cityC,Co is usually read as the relation "C is a city of Co", which is fine for computational purposes, but this reading obscures the notion that the object C is being typed as a city; more precisely, it is being typed as a city of Co.

in order to make this reading more apparent, we will write the grammar as: we allow the categories to be indexed by terms (a move which is a kind of "currying" of a relation into a type for its first argument).


In work by Wiebe, Bruce, and O'Hara (Wiebe et al., 1999;Bruce and Wiebe, 2000), a corpus of 1,001 sentences 3 of the Wall Street Journal TreeBank Corpus (Marcus et al., 1993) was manually annotated with subjectivity classifications.

each sentence was assigned a subjective or objective classification, according to concensus tags derived by a statistical analysis of the classes assigned by three human judges (see (Wiebe et al., 1999) for further information).


But The method proposed by (Mikheev 1996) has its own weaknesses, in that it is restricted in scope.

it aims to acquire rules for unknown words in corpora from their ending characters without looking at the context.


The transition is traversed if its input symbol matches the current symbol in the input and generates the output symbol associated with the transition.

an FST can be regarded as a 2-tape FSA with an input tape from which the input symbols are read and an output tape where the output symbols are written.


Let's come back to the three sentences from Figure 1: we want to capture our subjective ranking of the sentences with appropriate indicator values.

we want the machine to be able to see differences which a human might see.


The most extreme case was to learn the difference between Japanese text (in native character encoding) and English.

we have come up with a very computationally expensive method for Language Identification.


The model utilizes not only attributes about bunsetsus i, j but also attributes about all bunsetsus (including j) which follow bunsetsu i.

instead of learning two output values "T(true)" or "F(false)" for the dependency between two bunsetsus, three output values are used for learning: the bunsetsu i is "bynd (dependent on a bunsetsu beyond j)", "dpnd (dependent on the bunsetsu j)" or "btwn (dependent on a bunsetsu between i and j)".


Existing statistical parsers are quite efficient compared to grammar-based systems.

our system used an HPSG-based grammar, whose speed is said to be slow.


It means that surrounding alphabets give more information than previous output.

e-K transliteration is not the alphabet-per-alphabet or phoneme-perphoneme classi cation problem.


Sentence 3 exhibits a typical structural ambiguity in a Japanese sentence.

iro wa 'color topic-marker' possibly modifies kite-iru 'wear' or awaseru 'harmonize'.


It is obvious that such small data are insufficient for automatic case frame learning.

case frame learning must be done from enormous unanalyzed corpora, in unsupervised way 2 .


As a result, 565 modifier-head relations differed; in 260 cases, our method was correct and the original KNP was incorrect (by considering the structures in the Kyoto University Corpus as a golden standard); in 224 cases, vice versa.

our method was superior to KNP by 36 cases, and increased the overall accuracy from 89.8% to 89.9%.


In this paper, words and sentences play complementary roles.

a sentence is represented by the set of words it contains, and a word by the set of sentences in which it appears.


To compute the similarities, we initialize WSM n to the identity matrix.

each word is fully similar (1) to itself and completely dissimilar (0) to other words.


As a result, even for when the word on the left is the same, the tagging results will be dierent if the complete contexts are dierent.

the neuro tagger can hardly acquire the rules with single inputs.


Furthermore, although lexical information is very important in tagging, it is dicult for neural nets to use it because doing so would make the network enormous.

the neuro tagger cannot acquire the rules with lexical information.


In this paper, suppose there is a lexicon V , where the POSs that can be served by each word are listed, and there is a set of POSs, 0.

unknown words that do not exist in the lexicon are not dealt with.


Even when the POS of a word can be determined with certainty by only the word on the left, for example, the neuro tagger still tries to tag based on the complete context.

in general, what the neuro tagger can easily acquire by learning is the rules whose conditional parts are constructed by all inputs ipt x (x = t 0 l;1 11 ; t + r) that are joined with an AND logical operator, i.e., (ipt t0l & 1 11ipt t & 1 1 1ipt t+r !


Also, although lexical information is very important in tagging, it is dicult for the neuro tagger to use it, because doing so would make the network enormous.

the neuro tagger cannot acquire rules whose conditional parts consist of lexical information like (w !


By examining the acquired rules that were obtained by applying the most complete template set, i.e., the set used in case (2) described above, we found that 99.9% of them were those that can be obtained by applying the original set of templates.

the acquired rules were almost those that are dicult for the neuro tagger to acquire.


But in order to recognize this relation, we need to recognize that the two desires given in units A 2 and B 2 are incompatible, which is captured by the CON-TRAST relation that holds between the two units.

the intention associated with segment A 2 ; B 2 is a function both of its nuclei, A 2 and B 2 , and of the rhetorical relation of CONTRAST that holds between them.


Since it is impossible to determine a priori the spans that will be used to make up a discourse tree, we will associate with each span that could possibly become part of a tree a status, a type, promotion, and primary intention relation and let discourse and intentional constraints determine the valid discourse trees.

we want to determine from the set of n + n , 1 + n , 2 + : : : + 1 = nn + 1 =2 potential spans that pertain to a sequence of n discourse units, the subset that adheres to some constraints of rhetorical and intentional well-formedness.


The status, type, and promotion set that are associated with a discourse segment reflect the compositionality criterion.

whenever a rhetorical relation holds between two spans, either a similar relation holds between the most salient units of those spans or an extended rhetorical relation holds between those spans.


In contrast, axiom (9) explicates the relationship between intentional dominance and discourse structure.

if we know that the intention associated with span l 1 ; h 1 dominates the intention associated with span l 2 ; h 2 , then both these spans play an active role in the representation and, moreover, the segment l 2 ; h 2 plays a SATELLITE role.


Due to the flexibility of the CHR system, one can now use the constraint propagation approach to drive other constraint solving or constraint resolution techniques (also implemented in CHR) resulting in a homogenous environment which combines both classical constraint solving with a more operational generator.

one can use each created edge to post other constraints, for example about the well-formedness of associated typed feature structures.


By posting them, they become available for other constraint handling rules.

systems directly implementing HPSG seem to suffer from the problem how to drive the constraint resolution process efficiently.


3.6 Method 2 (using category-exclusive rules with the highest similarity) Method 2 combines the example-based method and Method 1.

it combines the method using similarity and the method using category-exclusive rules in order to eliminate the meaningless categoryexclusive rules mentioned in the previous section.


However, when category-exclusive rules having more than one frequency exist, the above procedure is performed after eliminating all of the categoryexclusive rules having one frequency.

category-exclusive rules having more than one frequency are given a higher priority than categoryexclusive rules having only one frequency but having a higher similarity.


This makes it possible to represent universal quantification, implication, negation and other logical operators.

shows how an SSN can be mapped into a Discourse Representation Structure (DRS; Kamp & Reyle, 1993), thus providing a formal semantic interpretation of SSNs.


In formal terms, a model is defined as follows: A model M is a quintuple hU; E; Pred; Quant; Namei (V) A function Name mapping constants to members of U.

the constants c P , where P is a predicate are mapped to P r e d P , i.e., the supremum, also known as the sum, of the interpretation of P. Notice that in our models there are separate domains for objects and eventualities (i.e., states and events).


The networks which we have used are an extension of commonly used OO networks for natural language generation.

our networks cover quantificational plural noun phrases such 'most N', 'few N', etc.


If necessary, we can come back to the -term expression: in plive j.

the syntactic proof net yields a term t expressing how the elementary parts combine (in this case t = abcd).


The probability of selecting a subtree pair t s t t is calculated by dividing the frequency of the subtree pair in the bag by the number of subtrees that have the same root node labels in this bag.

let t s t t be the number of times the sub-tree pair t s t t occurs in the bag of subtree pairs, and r´tµ be the root node categories of t, then the probability assigned to (1) Given the assumptions that all subtree pairs are independent, the probability of a derivation t s 1 t t 1 AE ¡ ¡ ¡ AE t s N t t N is equal to the product of the probabilities of the used subtree pairs.


If a node p has a daughter node d, then p must have a text-level one rank higher than d, u nless both nodes have the minimal level 0.

either (From this it follows that any nodes that are sisters must have the same level.)


Our method employs minimal but precise contextual information for disambiguation.

only information provided by the target-verb, enriched by the condensed information of a probabilistic class-based lexicon, is used.


For example, from the occurrences of the transitive verb absolvovat ("go through something") that occurred ten times in the corpus, no occurrence consisted of the verb-object pair alone.

the correct SF constituted 0% of the observed situations.


For each verb, we need to associate a score to the hypothesis that a particular set of dependents of the verb are arguments of that verb.

we need to assign a value to the hypothesis that the observed frame under consideration is the verb's SF.


Using the definitions from Section 3.1 we can compute t-scores using the equation below and use its value to measure the association between a verb and a frame observed with it.

where, the hypothesis being tested using the t-score is whether the distributions p 1 and p 2 are not independent.


(Lloyd, 1987) having variables with type information.


We assume the same de nitions as the category above and that the two lists lie on the same strength scale.

an implication endorsed as reliable is the same strength as a P i endorsed as reliable and stronger than a P i endorsed as hypothesis.


Since many belief revision systems would prefer to keep the old belief, revising the least amount possible Logan et al., 1994, the existing beliefs will be relatively resistant to revision, and will only be displaced by an inconsistent belief of higher endorsement.

the doubted agent can persuade the doubting agent b y convincing her that he is more expert than the old source.


Since the doubted agent has to infer that P i implies the negation of the doubted proposition, the doubting agent should give a clue that she is expressing doubt when she expects the implication to be non-obvious.

if the implication relies on information that is known only to her or information which she has little evidence for and therefore little cause to believe that the other agent believes the same, she should use a clue word.


This suggests that we have missed something important.

from our investigation of the result, we believe that coordinate structure is one of the most important factors to improve the accuracy.


So if this is the output, we don't record the edge as it is the default.

if there is no edge information for a particular input at a particular state, the output is the next bunsetsu (or 1).


There are a number of efforts to improve the accuracy of Japanese dependency analysis (Haruno et.al, 1997) (Fujio, Matsumoto, 1998) (Kanayama et.al, 2000).

it is interesting to see that Kanayama's method uses a similar idea, limiting the head candidates, in order to improve the accuracy.


Instead, the word-breaker returns all possible words that span the given string in a word lattice, and the best word sequence is determined by applying the syntactic rules for building parse trees.

there is no task of selecting the best segmentation per se; the best word-breaking analysis is merely a concomitant of the best syntactic parse.


We chose a spoken dialog corpus that includes demonstrative pronouns because demonstratives are both a prominent feature of problem-solving dialogs and a sorely neglected field of study.

we asked two questions: Do Speakers Signal Antecedent Properties Acoustically?


An assumption made by all the methods mentioned in this paper is that the members of the test set are all independent of one another.

knowing how a method performs on one test set sample should not give any information on how that method performs on other test set samples.


4 The subdiagrams are arranged vertically and enclosed by a pair of square brackets."

var elements stand for a variable subpart of a diagram and indicate the type of diagram that can appear at that location.


A further distinguishing characteristic of functional and content terms is that different texts will often vary in their content but tend to share a common linguistic structure and therefore make similar use of functional terms.

the probability distribution of content terms will vary from text to text, but the distribution of function terms will not.


Not every L Allen formula corresponds to an evaluable SQL Temporal query.

formulae might h a ve an in nite answer.


The paper describes a module that serves as a back-end to such at analysis methods and transforms their output into full semantic representations as constructed by deep analysis methods.

the module has been designed so as to process input from tree banks a statistic context-free parser trained on these tree banks a nite-state parser a traditional feature-structure parser The semantic representations which the module constructs are so-called Verbmobil Interface Terms VITs Bos et al., 1998, building on Reyle's Underspeci ed Discourse Representation Structures 1993, see an example in Figure 1.


The definition was created based on the MUC/MET definition; however, the process of making the definition was not easy.

the definition of the newly introduced NE type "artifact" was controversial.


In contrast, the accuracy of the other types of NE is not so good.

artifacts are quite difficult to identify.


However, the fully automatic system has a problem in its coverage.

the training data was newspaper articles published in 1994 and the test data was from 1999, so there are several new names, e.g.


The multilingual phonological entry for gram is defined by sharing identical segments occurring in the majority of the language-specific entries (/grfm/ -/xrAm/ -/gram/).

onset 1 is /g/, onset 2 is /r/, and coda is /m/.


Rerunning the TR system rule learning with an information organization closer to the MB system produces the same 47% F-score as the MB system (recall is lower, but precision is higher).

we got this result when the TR system was rerun with no information on pp-attachments, verb chunk properties (e.g., passive, infinitive), named-entity labels or headword stems.


So with type 2 GRs, MB system's better performance seems to be mainly due its ability to differentiate the potential GRs by the feature of the number of verb chunks crossed by a GR.

making this and a few other changes to the TR system increases its F-score to the MB system's F-score, and the other changes (removing certain information) does not have a significant effect.


Since no syntactic knowledge is used in SBTG, output grammaticality can not be guaranteed.

if the corresponding constituents appear in the same order in both languages, both straight and inverted, then lexical matching does not provide the discriminative leverage needed to identify the sub-constituent boundaries.


Finally, a predefined threshold, TH decision , is employed to decide whether two stories discuss the same topic or not.

two stories are on the same topic if their similarity is larger than the predefined threshold.


Story expansion with the non-relevant terms would reduce the performance of a link detection system.

it may introduce some noise into the story and make the detection more difficult.


If a Chinese word corresponds to more than one English word, these English words are all selected.

we did not disambiguate the meaning of a Chinese word.


We now compare with the isolated strategy, which separately conduct chunks for English and Chinese.

in isolated strategy, we carry out the English and Chinese chunking separately, we call this experiment M. We next add the crossing constraint to M. chunk each language under the crossing constraint, without considering the chunking procedure of the correspondent language.


The  , using a translation dictionary and the EM algorithm; create a TF-IDF vector using contexts containing c ; create a TF-IDF vector   The uses of EM-NBC-Ensemble and EM-TF-IDF can be viewed as extensions of existing methods for word or phrase translation using non-parallel corpora.

the use of the EM Algorithm can help to accurately transform a frequency vector from one language to another.


With EM, vector A in Figure 5 is transformed into vector C, which is much closer to vector D, as expected.

eM can split the frequency of a word in english and distribute them into its translations in Chinese in a theoretically sound way (cf., the distributed frequencies of 'internet').


In order to further boost the performance of translation, we propose to also use the translation method proposed in Nagata et al.

we combine our method with that of Nagata et al by using a back-off strategy.


Table  8 shows the results -a scope of 69.2% with regard to the nouns that appear in the text (47.6% of the total number of different common nouns contained in the corpus).

after carrying out a very minor manual operation, we managed to label two out of every three nouns that appear in the corpus.


local assignment of PoS-tags is if not correct then at least (morphologically) plausible.

the first errors to be corrected are those where the assignment of PoS-tags violates morphological (and possibly other local, e.g., phonological) laws of the language.


As an example of such an as yet "undetectable" error in German we might take the configuration where two words tagged as finite verbs are separated from each other by a string consisting of nouns, adjectives, articles and prepositions only.

such a configuration is erroneous since the rules of German orthography require that some kind of clause separator (comma, dash, coordinating conjunction) occur inbetween two finite verbs 5 .


Thus, for example, in both our examples the property of being an impossible configuration is conserved if an adverb is placed inbetween, creating thus an "impossible trigram".

in the first example, the configuration PREP ADV VFIN cannot be a valid trigram, exactly for the same reasons as PREP VFIN was not a valid bigram: ADV is not a valid NP remnant.